MANAGERIAL ECONOMICS

SUPPOSE YOU ARE THE MARKETING MANAGER OF BAYER AND COMPANY AHMEDABAD, WHICH ARE THE TECHNIQUES YOU WILL APPLY IN FORECASTING DEMAND OF A PRODUCT YET TO BE MANUFACTURED.

AS A MARKETING MANAGER OF BAYER AND COMPANY , I WILL APPLY FOLLOWING TECHNIQUES IN FORECASTING DEMAND OF A PRODUCT YET TO BE MANUFACTURED :

Demand Forecasting Methodology

To obtain the most accurate forecasting results, market response forecasting and time series forecasting are used together to predict a demand.

Market response forecasting

To accurately predict the consequences of your choices, you must factor how the market will respond to each decision. For example, you need to forecast how consumers will react to various prices. Such a model will look like a textbook demand curve, as shown in Figure 1. The curve itself—labeled D1—predicts demand at different prices, holding other variables constant. At the regular price of Rs 265, it predicts weekly demand for about 1,800 boxes of our product in a specific market. If this item were discounted to Rs195, without any other stimuli changing,demand would increase to about 5,000 units.

The relationship between the quantity demanded and the price offered is expressed as the price elasticity of demand. Given the price elasticity, and a forecast of demand at a particular price, it is possible to forecast demand at alternative prices.

In addition to the price elasticity, it is also necessary to know or assume the precise mathematical relationship (the functional form). A multiplicative relationship, with constant elasticity, is a standard and successful assumption.

Time series forecasting

Pricing and promotion decisions greatly influence future demand, so calculating and predicting these outcomes is vital to realizing potential. Time series forecasting is a collection of methods for projecting forward from historic observations. A very simple example is a moving average. Of course, different methods are appropriate for different business conditions. The Holt’s Method is most suitable for basic or staple merchandise, while the Winter’s Method works best for seasonal merchandise, and Croston’s Method is appropriate for merchandise with little turnover. In all, there are more than a dozen methods to use, depending on your current situation. What is common across all methods is that the only data consumed in producing the forecast is derived of the learnings from previous similar situations. They permit modeling seasonal demand fluctuations, trend growth or decay, and lifecycle phenomena.

Using time series methods, you need to utilize prior observations of demand. A good source of these observations is a point-of-sale system. These systems capture sales/transaction information, so it is necessary to make two adjustments in order to create your time series forecast. The first is to adjust the sales quantity to reflect the sales that you could have achieved if there had been no inventory defects. This may be as simple as extrapolating across weeks in which the item was out of stock, or as complex as dynamically adjusting sales when daily stock values fell below presentation or size count thresholds.

For the second adjustment, you need a way to back out the effects of market stimuli on your observed sales. You can use the market response model, not to forecast the future, but to estimate what unconstrained historical sales would have been if price and other stimuli were held constant over the period of history under examination. This process is sometimes referred to as normalization. You can then apply appropriate time series methods to that history. The forecast predicts the true or unconstrained demand at regular price, assuming no promotional tactics are employed for each period in the future.

Shaping Demand

Thus far, you have predicted unconstrained demand (as separate from sales) and have looked at the impact of time and market influences on that demand. With this information in hand, you can effectively shape and respond to demand.

Here are some of the ways Marketing Managers use this information to manipulate demand for products.

1) Reassemble the pieces . Knowing how pricing and promotional tactics affect demand allows you to make better decisions regarding pricing levels and markdowns, which products to promote when, and what promotional tactics to employ—all in the service of achieving

your business objectives—whether they are increasing profits, market share, or revenue.

2) Predict promotional sales lifts . Merging the response knowledge with forward-looking promotional and pricing plans allows you to make better buying, allocation, and replenishment decisions, thereby reducing the cost of over-stocks and minimizing the frequency of out-of-stocks.

On both the demand and supply side of the profit equation, understanding the constituents of demand provides tangible financial benefits.

Demand Driven Execution

What happens once demand is understood? Having an accurate prediction of future demand might be interesting, but is relatively worthless unless you put it to practical use. To benefit retail organizations, demand intelligence should now become an integral component of planning and execution.

Plans based on historical sales only perpetuate mistakes made in the past, because demand intelligence is unconstrained and not influenced by events and inventory. It is more accurate than isolated point-of-sale data at representing the voice of the consumer. In this way, demand intelligence provides an additional point of view that quantifies the real potential of various categories, locations, and products.

You can derive achievable sales by integrating the demand forecast into each of the planning and execution processes.

The Marketing Manager mixes the “arts” of market trend analysis and merchandising with the “science” of demand forecasting. Once you understand anticipated sales, you can finalize your plan by incorporating the product mix, required weekly inventory levels, corresponding receipt flow, and exit strategies. You should repeat this process at each planning stage—from merchandise financial planning to assortment and item planning.

Finally, you will execute your demand driven plan—executing pricing decisions,generating orders, and notifying suppliers. You incorporate marketing plans,determine in-store product positioning, and allocate and replenish individual items.However, at the start of all of these executables was a consumer driven forecast that was transformed into an intelligent sales plan.

CONCLUSION

Demand forecasting allows Marketing Managers to make better decisions about which prices to adjust and when, which products to promote, and what promotional tactics to deploy, in order to achieve objectives. The benefits are significantly more profound and productive than a simple sales forecast.

The best informed decisions will help you increase profits, sales or market share— whatever your goal. By combining your knowledge of past performance under similar circumstances with forward-looking promotional pricing plans, you can make better buying, allocation, and replenishment decisions. In turn, you will reduce the cost of over-stocks and minimize the frequency of out-of-stocks.

Understanding consumer expectations at given times and under different market conditions delivers tangible benefits—both on the demand side and supply side of your business.

Understanding of demand should be the logical backbone of all merchandising decisions you make. Quantifying these effects is a formidable challenge, although the basic scientific techniques are well understood.

But even with rough-and-ready estimates of the magnitude of these effects—and how they vary under different circumstances—you are better equipped to make wise pricing and promotional decisions and to ensure that your supply and replenishment operations are calibrated to the sales impact of these decisions.

AS A MARKETING MANAGER OF BAYER AND COMPANY , I WILL APPLY ABOVE TECHNIQUES IN FORECASTING DEMAND OF A PRODUCT YET TO BE MANUFACTURED